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This paper proposes a method of photovoltaic power generation probability interval prediction based on Gaussian mixture model. The method first uses the K-means algorithm to divide historical photovoltaic power generation data by weather, uses a Gaussian mixture model to fit the divided prediction errors, and uses an expectation maximization algorithm to estimate model parameters. Predict the probability interval of photovoltaic power generation by calculating the confidence interval under the specified confidence level. The simulation results show that the performance evaluation indexes of the proposed method are better than the typical single distribution model when forecasting the photovoltaic power interval, which proves the accuracy and applicability of the proposed method.